| _version_ | 1866902304314097664 |
|---|---|
| author | Sneha P Sugitha S Sahithi P |
| author_facet | Sneha P Sugitha S Sahithi P |
| contents | <p class="MsoNormal"><em><span>Agriculture remains a key way people survive across many rural areas where small farms dominate. Still, missing quick access to knowledge, expert tips, or new tools often leads to weak harvests and money troubles. A smart farming guide can offer custom advice using live data like what crops to grow, when to water, how to handle pests, or best fertilizer use. It pulls info from internet connected sensors, local weather trends, and ground checks to track field conditions as they change. By applying learning models, it builds flexible suggestions based on past patterns plus current surroundings. Farmers get local-language updates via phone or website, making it simple to use no matter their background. Because the system fits how people actually work, choices become smarter and more practical over time. As a result, growing food sustainably gets easier, which helps boost harvests without harming land. Over months, many small-scale growers see real gains in what they earn from farming.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19553882 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smart Crop Advisory for Small and Marginal Farmers Sneha P Sugitha S Sahithi P IoT Sensors, Machine Learning <p class="MsoNormal"><em><span>Agriculture remains a key way people survive across many rural areas where small farms dominate. Still, missing quick access to knowledge, expert tips, or new tools often leads to weak harvests and money troubles. A smart farming guide can offer custom advice using live data like what crops to grow, when to water, how to handle pests, or best fertilizer use. It pulls info from internet connected sensors, local weather trends, and ground checks to track field conditions as they change. By applying learning models, it builds flexible suggestions based on past patterns plus current surroundings. Farmers get local-language updates via phone or website, making it simple to use no matter their background. Because the system fits how people actually work, choices become smarter and more practical over time. As a result, growing food sustainably gets easier, which helps boost harvests without harming land. Over months, many small-scale growers see real gains in what they earn from farming.</span></em></p> |
| title | Smart Crop Advisory for Small and Marginal Farmers |
| topic | IoT Sensors, Machine Learning |
| url | https://doi.org/10.5281/zenodo.19553882 |